Reference and the facilitation of search in spatial domains

This is a pre-final version of the article, whose official publication is expected in the winter of 2013-14.Peer reviewedPreprin

Production of Referring Expressions for an Unknown Audience : a Computational Model of Communal Common Ground

The research reported in this article is based on the Ph.D. project of Dr. RK, which was funded by the Scottish Informatics and Computer Science Alliance (SICSA). KvD acknowledges support from the EPSRC under the RefNet grant (EP/J019615/1).Peer reviewedPublisher PD

Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments

Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is still mostly limited to rule-based methods. In this work, we propose a two-stage approach that relies on deep learning for estimating spatial relations to describe an object naturally and unambiguously with a referring expression. We compare our method to the state of the art algorithm in ambiguous environments (e.g., environments that include very similar objects with similar relationships). We show that our method generates referring expressions that people find to be more accurate ($\sim$30% better) and would prefer to use ($\sim$32% more often).Comment: International Conference on Intelligent Robots and Systems (IROS 2019), Demo 1: Finding the described object (https://youtu.be/BE6-F6chW0w), Demo 2: Referring to the pointed object (https://youtu.be/nmmv6JUpy8M), Supplementary Video (https://youtu.be/sFjBa_MHS98

A Joint Speaker-Listener-Reinforcer Model for Referring Expressions

Referring expressions are natural language constructions used to identify particular objects within a scene. In this paper, we propose a unified framework for the tasks of referring expression comprehension and generation. Our model is composed of three modules: speaker, listener, and reinforcer. The speaker generates referring expressions, the listener comprehends referring expressions, and the reinforcer introduces a reward function to guide sampling of more discriminative expressions. The listener-speaker modules are trained jointly in an end-to-end learning framework, allowing the modules to be aware of one another during learning while also benefiting from the discriminative reinforcer's feedback. We demonstrate that this unified framework and training achieves state-of-the-art results for both comprehension and generation on three referring expression datasets. Project and demo page: https://vision.cs.unc.edu/referComment: Some typo fixed; comprehension results on refcocog updated; more human evaluation results adde

Salience and pointing in multimodal reference

Pointing combined with verbal referring is one of the most paradigmatic human multimodal behaviours. The aim of this paper is foundational: to uncover the central notions that are required for a computational model of human-generated multimodal referring acts. The paper draws on existing work on the generation of referring expressions and shows that in order to extend that work with pointing, the notion of salience needs to play a pivotal role. The paper investigates the role of salience in the generation of referring expressions and introduces a distinction between two opposing approaches: salience-first and salience-last accounts. The paper then argues that these differ not only in computational efficiency, as has been pointed out previously, but also lead to incompatible empirical predictions. The second half of the paper shows how a salience first account nicely meshes with a range of existing empirical findings on multimodal reference. A novel account of the circumstances under which speakers choose to point is proposed that directly links salience with pointing. Finally, a multidimensional model of salience is proposed to flesh this model out

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Does Size Matter – How Much Data is Required to Train a REG Algorithm?

In this paper we investigate how much data is required to train an algorithm for attribute selection, a subtask of Referring Expressions Generation (REG). To enable comparison between different-sized training sets, a systematic training method was developed. The results show that depending on the complexity of the domain, training on 10 to 20 items may already lead to a good performance

Building a Generation Knowledge Source using Internet-Accessible Newswire

In this paper, we describe a method for automatic creation of a knowledge source for text generation using information extraction over the Internet. We present a prototype system called PROFILE which uses a client-server architecture to extract noun-phrase descriptions of entities such as people, places, and organizations. The system serves two purposes: as an information extraction tool, it allows users to search for textual descriptions of entities; as a utility to generate functional descriptions (FD), it is used in a functional-unification based generation system. We present an evaluation of the approach and its applications to natural language generation and summarization.Comment: 8 pages, uses eps